def float_leg(df, payer_receiver, notional, spread, dsc_curve, fwd_curve, cal, day_count_basis, dirty_flag=True, roll_conv='modifiedfollowing'):
# Dirty valuation, include interest accrued before valuation date
if dirty_flag:
d1 = pd.DatetimeIndex(df['Start Date'])
d2 = pd.DatetimeIndex(df['End Date'])
df['Days'] = sch.day_count(d1, d2, day_count_basis)
df['Year Fraction'] = sch.year_fraction(d1, d2, day_count_basis)
# Clean valuation, exclude interest accrued before valuation date
else:
tmp = df['Start Date'].copy()
tmp[tmp<dsc_curve.curve_date] = dsc_curve.curve_date
d1 = pd.DatetimeIndex(tmp)
d2 = pd.DatetimeIndex(df['End Date'])
df['Days'] = sch.day_count(d1, d2, day_count_basis)
df['Year Fraction'] = sch.year_fraction(d1, d2, day_count_basis)
df['Notional'] = notional
fixing_dates, fixings = get_fwd_rates(DatetimeIndex(df['Start Date']),DatetimeIndex(df['End Date']),fwd_curve,cal,roll_conv)
df['Fixing Date'] = fixing_dates
df['Index Rate'] = fixings
df['Spread'] = spread / 10000.0
df['Floating Rate'] = df['Index Rate'] + df.Spread
df['Cash Flow'] = notional * df['Floating Rate'] * df['Year Fraction'] * payer_receiver
df['Discount Factor'] = dsc_curve.get_dcf(DatetimeIndex(df['Payment Date']))
df.loc[df['Payment Date'] == dsc_curve.curve_date,'Discount Factor'] = 0.0
df['Cash Flow PV'] = df['Cash Flow'] * df['Discount Factor']
return df
def get_fwd_rates(d1, d2, fwd_curve, cal, roll_conv):
# Get historical fixings
historical_dates = d1[d1 <= fwd_curve.curve_date]
historical_fixings = []
historical_fixing_dates = []
for i,d in enumerate(historical_dates):
if d in fwd_curve.historical_fixings.keys():
historical_fixings.append(fwd_curve.historical_fixings[d]/100.0)
historical_fixing_dates.append(d)
else:
j = 1
while j < 10:
d = DatetimeIndex([d])
d = pd.Timestamp(np.busday_offset(dates=d.astype(str), offsets=j, roll=roll_conv, busdaycal=cal)[0])
if d in fwd_curve.historical_fixings.keys():
historical_fixings.append(fwd_curve.historical_fixings[d]/100.0)
historical_fixing_dates.append(d)
break
j += 1
# Calculate future fixings
d2 = d2[d1 > fwd_curve.curve_date]
d1 = d1[d1 > fwd_curve.curve_date]
t = pd.Series(sch.year_fraction(d1,d2,fwd_curve.day_count_basis))
future_fixings = list((1 / t) * (fwd_curve.get_dcf(d1) / fwd_curve.get_dcf(d2) - 1))
return pd.Series(historical_fixing_dates + d1.to_list()), pd.Series(historical_fixings + future_fixings)
def fixed_leg(df, payer_receiver, notional, fixed_rate, dsc_curve, day_count_basis, dirty_flag=True):
# Dirty valuation, include interest accrued before valuation date
if dirty_flag:
d1 = pd.DatetimeIndex(df['Start Date'])
d2 = pd.DatetimeIndex(df['End Date'])
df['Days'] = sch.day_count(d1, d2, day_count_basis)
df['Year Fraction'] = sch.year_fraction(d1, d2, day_count_basis)
# Clean valuation, exclude interest accrued before valuation date
else:
tmp = df['Start Date'].copy()
tmp[tmp<dsc_curve.curve_date] = dsc_curve.curve_date
d1 = pd.DatetimeIndex(tmp)
d2 = pd.DatetimeIndex(df['End Date'])
df['Days'] = sch.day_count(d1, d2, day_count_basis)
df['Year Fraction'] = sch.year_fraction(d1, d2, day_count_basis)
df['Fixed Rate'] = fixed_rate / 100.0
df['Notional'] = notional
df['Cash Flow'] = df['Notional'] * df['Fixed Rate'] * df['Year Fraction'] * payer_receiver
df['Discount Factor'] = dsc_curve.get_dcf(DatetimeIndex(df['Payment Date']))
df.loc[df['Payment Date'] == dsc_curve.curve_date,'Discount Factor'] = 0.0
df['Cash Flow PV'] = df['Cash Flow'] * df['Discount Factor']
return df
def __init__(self, name, curve_date, discount_data, day_count_basis, historical_fixings=None):
def calc_discount_factors(self,t,discount_factors,date_range, bps_shift):
# Construct the log-discount curve using cubic spline interpolation
log_discount = CubicSpline(t,-np.log(list(discount_factors)) + bps_shift*t) # basis point shift to the zero curve
return np.exp(-log_discount(pd.Series(sch.year_fraction(self.curve_date,date_range,self.day_count_basis))))
# Store the defining terms of the curve
self.name = name
self.curve_date = curve_date
self.discount_data = discount_data
self.day_count_basis = day_count_basis
# Calculate the time in years from the curve_date to the relevant dates of discount factors
d = DatetimeIndex(discount_data.keys())
t = np.array(sch.year_fraction(DatetimeIndex([curve_date for _ in range(len(d))]),d,day_count_basis))
# Dates to precalc discount factors for
date_range = pd.date_range(self.curve_date,pd.Timestamp('2059-12-31'),freq='d')
discount_factors = calc_discount_factors(self,
t=t,
discount_factors=discount_data.values(),
date_range=date_range,
bps_shift=0)
self.discount_factors_base = pd.DataFrame({'Dates': date_range, 'DiscountFactors': discount_factors})
discount_factors = calc_discount_factors(self,
t=t,
discount_factors=discount_data.values(),
date_range=date_range,
bps_shift=1/100/100)
self.discount_factors_upshift = pd.DataFrame({'Dates': date_range, 'DiscountFactors': discount_factors})
discount_factors = calc_discount_factors(self,
t=t,
discount_factors=discount_data.values(),
date_range=date_range,
bps_shift=-1/100/100)
self.discount_factors_downshift = pd.DataFrame({'Dates': date_range, 'DiscountFactors': discount_factors})
self.discount_factors = self.discount_factors_base
if historical_fixings is not None:
self.historical_fixings = historical_fixings
def calc_discount_factors(self,t,discount_factors,date_range, bps_shift):
# Construct the log-discount curve using cubic spline interpolation
log_discount = CubicSpline(t,-np.log(list(discount_factors)) + bps_shift*t) # basis point shift to the zero curve
return np.exp(-log_discount(pd.Series(sch.year_fraction(self.curve_date,date_range,self.day_count_basis))))